README For Learning from Null Effects: A Bottom-Up Approach

Analysis performed on:
Mac OS
1.1 GHz Intel Core i5, 8 GB 3733 MHz LPDDR3
macOS Big Sur 11.2.1

Programs used:
R version 3.6.3 (2020-02-29)
Stata, Stata/IC 14.2 for Mac (64-bit Intel)


R packages used:
ggplot2 3.3.3
ggthemes 4.2.0
readxl 1.3.1
dplyr 1.0.2.9000
stringr 1.4.0
psych 1.9.12.31
estimatr 0.22.0
foreign 0.8.76
RColorBrewer 1.1.2
gridExtra 2.3

Run time
aea_plot.R: 2.166733 secs
nny_tables.do .94 seconds
nny_figure.R 0.03 seconds
coefplots_jordan.R 7.9 seconds
power_jordan.R 0.4 seconds 

Code scripts:
aea_plot.R
nny_tables.do
nny_figure.R
coefplots_jordan.R
power_jordan.R

Data files:
aea_nudges.xlsx
nny_data.dta
jordan_svy.csv

Intermediary data file: 
nudgeeffects.txt


Output files:

aea_plot.R:
figure1.pdf


nny_tables.do:
tableB1.tex
tableB2.tex
tableB3.tex
tableB4.tex
tableB5.tex
tableB6.tex

nny_figure.R:
figureB1.pdf

coefplots_jordan.R:
figureC1.pdf
figureC2.pdf
figureC3.pdf
figureC4.pdf
figureC5.pdf
figureC6.pdf
figureC7.pdf
figureC8.pdf
figureC9.pdf
figureC10.pdf
figureC11.pdf

power_jordan.R:
figureC12.pdf

 
Instructions:
1- Run aea_plot.R to produce figure1.pdf
2- Run nny_tables.do to produce the tables in appendix B
3- Run nny_figure.R to produce figureB1.pdf
4- Run coefplots_jordan.R to produce the figures in coefplots appendix C (figures C1-C11)
5- Run power_jordan.R to produce figureC12.pdf

